Fuzzy logic controller for energy management of power split hybrid electrical vehicle transmission

2014 ◽  
Author(s):  
Varan Navale ◽  
Timothy C. Havens
Keyword(s):  
Fuzzy Logic ◽  
Energy Management ◽  
Fuzzy Logic Controller ◽  
Power Split ◽  
Electrical Vehicle ◽  
Hybrid Electrical Vehicle

scholarly journals Experts versus Algorithms? Optimized Fuzzy Logic Energy Management of Autonomous PV Hybrid Systems with Battery and H2 Storage

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1777
Author(s):  
Lisa Gerlach ◽  
Thilo Bocklisch
Keyword(s):  
Fuzzy Logic ◽  
Energy Management ◽  
Fuzzy Logic Controller ◽  
Expert Knowledge ◽  
Large Power ◽  
Hybrid Energy ◽  
Knowledge Based ◽  
Long Term Storage ◽  
Fuzzy Logic Approach

Off-grid applications based on intermittent solar power benefit greatly from hybrid energy storage systems consisting of a battery short-term and a hydrogen long-term storage path. An intelligent energy management is required to balance short-, intermediate- and long-term fluctuations in electricity demand and supply, while maximizing system efficiency and minimizing component stress. An energy management was developed that combines the benefits of an expert-knowledge based fuzzy logic approach with a metaheuristic particle swarm optimization. Unlike in most existing work, interpretability of the optimized fuzzy logic controller is maintained, allowing the expert to evaluate and adjust it if deemed necessary. The energy management was tested with 65 1-year household load datasets. It was shown that the expert tuned controller is more robust to changes in load pattern then the optimized controller. However, simple readjustments restore robustness, while largely retaining the benefits achieved through optimization. Nevertheless, it was demonstrated that there is no one-size-fits-all tuning. Especially, large power peaks on the demand-side require overly conservative tunings. This is not desirable in situations where such peaks can be avoided through other means.

scholarly journals Embedded Fuzzy Logic Controller and Wireless Communication for Home Energy Management Systems

Electronics ◽  
2018 ◽  
Vol 7 (9) ◽  
pp. 189 ◽  
Author(s):  
Aryuanto Soetedjo ◽  
Yusuf Nakhoda ◽  
Choirul Saleh
Keyword(s):  
Fuzzy Logic ◽  
Energy Management ◽  
Cost Reduction ◽  
Fuzzy Logic Controller ◽  
Management Systems ◽  
Fuzzy Membership ◽  
Membership Functions ◽  
Energy Management Systems ◽  
Fuzzy Membership Functions ◽  
Home Energy Management

Energy management systems in residential areas have attracted the attention of many researchers along the deployment of smart grids, smart cities, and smart homes. This paper presents the implementation of a Home Energy Management System (HEMS) based on the fuzzy logic controller. The objective of the proposed HEMS is to minimize electricity cost by managing the energy from the photovoltaic (PV) to supply home appliances in the grid-connected PV-battery system. A fuzzy logic controller is implemented on a low-cost embedded system to achieve the objective. The fuzzy logic controller is developed by the distributed approach where each home appliance has its own fuzzy logic controller. An automatic tuning of the fuzzy membership functions using the Genetic Algorithm is developed to improve performance. To exchange data between the controllers, wireless communication based on WiFi technology is adopted. The proposed configuration provides a simple effective technology that can be implemented in residential homes. The experimental results show that the proposed system achieves a fast processing time on a ten-second basis, which is fast enough for HEMS implementation. When tested under four different scenarios, the proposed fuzzy logic controller yields an average cost reduction of 10.933% compared to the system without a fuzzy logic controller. Furthermore, by tuning the fuzzy membership functions using the genetic algorithm, the average cost reduction increases to 12.493%.

Experimental validation of a type-2 fuzzy logic controller for energy management in hybrid electrical vehicles

2013 ◽  
Vol 26 (7) ◽  
pp. 1772-1779 ◽  
Author(s):  
Javier Solano Martínez ◽  
Jérôme Mulot ◽  
Fabien Harel ◽  
Daniel Hissel ◽  
Marie-Cécile Péra ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Energy Management ◽  
Experimental Validation ◽  
Fuzzy Logic Controller ◽  
Electrical Vehicles

scholarly journals Analysis and Experiment of a Novel Brushless Double Rotor Machine for Power-Split Hybrid Electrical Vehicle Applications

Energies ◽  
2013 ◽  
Vol 6 (7) ◽  
pp. 3209-3223 ◽  
Author(s):  
Ping Zheng ◽  
Qian Wu ◽  
Jingang Bai ◽  
Chengde Tong ◽  
Zhiyi Song
Keyword(s):  
Power Split ◽  
Electrical Vehicle ◽  
Hybrid Electrical Vehicle

scholarly journals An Energy Management System for Residential Autonomous DC Microgrid Using Optimized Fuzzy Logic Controller Considering Economic Dispatch

Energies ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 1457 ◽  
Author(s):  
Shehab Al-Sakkaf ◽  
Mahmoud Kassas ◽  
Muhammad Khalid ◽  
Mohammad A. Abido
Keyword(s):  
Fuzzy Logic ◽  
Energy Storage ◽  
Fuel Cell ◽  
Wind Turbine ◽  
Energy Management ◽  
Fuzzy Logic Controller ◽  
Economic Dispatch ◽  
Energy Management System ◽  
Battery Energy Storage ◽  
Battery Energy

This work presents the operation of an autonomous direct current (DC) DC microgrid for residential house controlled by an energy management system based on low complexity fuzzy logic controller of only 25-rules to manage the power flow that supply house load demand. The microgrid consists of photovoltaic (PV), wind turbine, fuel cell, battery energy storage and diesel generator. The size of the battery energy storage is determined based on the battery sizing algorithm depending on the generation of renewables during all seasons of the year in the eastern region of Saudi Arabia. Two scenarios are considered in this work. In the first scenario: the microgrid consists of solar PV, wind turbine, battery energy storage and fuel cell. The fuzzy logic controller is optimized using an artificial bee colony technique in order to increase the system energy saving efficiency and to reduce the cost. In the second scenario: wind turbine is replaced by a diesel generator, also the rated power of the fuel cell is reduced. In this scenario, a new method is proposed to reduce the generation cost of the dispatchable sources in the microgrid by considering economic dispatch within the optimized fuzzy logic energy management system. To obtain the most suitable technique for solving the economic dispatch problem, three optimization techniques were used which are particle swarm optimization, genetic algorithm and artificial bee colony based on real environmental data and real house load demand. A comparison in terms of energy saving between the two scenarios and a comparison in terms of cost reduction between conventional economic dispatch method and the proposed method are presented.

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